Tethered Oligonucleotide-Primed Sequencing, TOP-Seq: A High-Resolution Economical Approach for DNA Epigenome Profiling.

Modification of CG dinucleotides in DNA is part of epigenetic regulation of gene function in vertebrates and is associated with complex human disease. Bisulfite sequencing permits high-resolution analysis of cytosine modification in mammalian genomes; however, its utility is often limited due to substantial cost. Here, we describe an alternative epigenome profiling approach, named TOP-seq, which is based on covalent tagging of individual unmodified CG sites followed by non-homologous priming of the DNA polymerase action at these sites to directly produce adjoining regions for their sequencing and precise genomic mapping. Pilot TOP-seq analyses of bacterial and human genomes showed a better agreement of TOP-seq with published bisulfite sequencing maps as compared to widely used MBD-seq and MRE-seq and permitted identification of long-range and gene-level differential methylation among human tissues and neuroblastoma cell types. Altogether, we propose an affordable single CG-resolution technique well suited for large-scale epigenome studies.

Proliferative verrucous and homogeneous Leukoplakias exhibit differential methylation patterns.

OBJECTIVE: Proliferative verrucous leukoplakia (PVL) is considered a clinically distinct entity from other oral leucoplakias (OLs) due to its clinical presentation and evolution. However, molecular differences between them remain unclear. We aimed to determine whether there are methylation differences between PVL and other forms of OLs. MATERIALS AND METHODS: Oral biopsies from 12 patients with PVL, eight patients with homogeneous leucoplakia (HL), and 10 healthy individuals were obtained for a genome-wide DNA methylation analysis via the Infinium EPIC Platform. RESULTS: A total of 1815 differentially methylated CpGs were found between PVL and HL, with a prominent state of hypermethylation in HL patients. CpGs covered 813 genes with distinct roles, including cell adhesion, extracellular matrix organization, and cell and synaptic signaling. 43% of these genes had been previously described in cancer and associated with prognosis. We developed a multinomial logistic regression model able to differentiate HL, PVL, and control samples. The model had a cross-validated estimate of 73% and included differentially methylated cancer-related genes between the pathological conditions and the healthy donors, including ADNP, BRCA2, CDK13, GNB1, NIN, NUMB, PIK3C2B, PTK2, SHISA4, THSD7B, WWP1, and ZNF292. It also included CpGs covering differentially methylated genes in HL (MEN1 and TNRC6B) and PVL (ACOXL, ADH1B, CAMTA1, CBFA2T3, CPXM2, LRFN2, SORCS2, and SPN). CONCLUSIONS: PVL and HL present differential methylation patterns that could be linked to their differential clinical behavior. Our findings show the potential of methylation markers and suggest novel diagnostic biomarkers.

Changes in m6A RNA methylation are associated with male sterility in wolfberry.

BACKGROUND: N6-methyladenosine (m6A) modification is the most abundant type of RNA modification in eukaryotic cells, playing pivotal roles in multiple plant growth and development processes. Yet the potential role of m6A in conferring the trait of male sterility in plants remains unknown. RESULTS: In this study, we performed RNA-sequencing (RNA-Seq) and m6A-sequencing (m6A-Seq) of RNAs obtained from the anther tissue of two wolfberry lines: 'Ningqi No.1' (LB1) and its natural male sterile mutant 'Ningqi No.5' (LB5). Based on the newly assembled transcriptome, we established transcriptome-wide m6A maps for LB1 and LB5 at the single nucleus pollen stage. We found that the gene XLOC_021201, a homolog of m6A eraser-related gene ALKBH10 in Arabidopsis thaliana, was significantly differentially expressed between LB1 and LB5. We also identified 1642 and 563 m6A-modified genes with hypermethylated and hypomethylated patterns, respectively, in LB1 compared with LB5. We found the hypermethylated genes significantly enriched in biological processes related to energy metabolism and lipid metabolism, while hypomethylation genes were mainly linked to cell cycle process, gametophyte development, and reproductive process. Among these 2205 differentially m6A methylated genes, 13.74% (303 of 2205) were differentially expressed in LB1 vis-à-vis LB5. CONCLUSIONS: This study constructs the first m6A transcriptome map of wolfberry and establishes an association between m6A and the trait of male sterility in wolfberry.

Association between placental DNA methylation and fetal congenital heart disease.

Congenital heart disease (CHD) is a worldwide problem with high morbidity and mortality. Early diagnosis of congenital heart disease is still a challenge in clinical work. In recent years, few studies indicated that placental methylation may be predictors of CHD. More studies are needed to confirm the association between placental methylation and CHD. The aim of this study was to investigate the association between prenatal placental DNA methylation and CHD. Placental tissues were obtained from four fetuses during the second trimester with isolated, non-syndromic congenital heart disease, including three cases with double outlet right ventricle (DORV) and one case with tetralogy of Fallot (TOF), and four unaffected fetuses as controls. The Illumina Infinium Human Methylation 850K BeadChip assay was employed to identify differential methylation sites (DMSs) and differential methylation regions (DMRs). Differential methylation was evaluated by comparing the ß-values for individual CpG loci in cases vs. controls. In addition, the function of genes was assessed through KEGG enrichment analysis, Gene Ontology (GO) analysis and KEGG pathway analysis. Compared with the control group, we identified 9625 differential methylation genes on 26,202 DMSs (p < 0.05), of which 6997 were hyper-methylation and 2628 were hypo-methylation. The top 30 terms of GO biological process and KEGG enrichment analysis of DMSs were connected with multiple important pathways of heart development and disease. Ten differentially methylated regions and the genes related to DMRs, such as TLL1, CRABP1, FDFT1, and PCK2, were identified. The deformity caused by the loss of function of these genes is remarkably consistent with the clinical phenotype of our cases. The DNA methylation level of placental tissue is closely associated with fetal congenital heart disease.

A comprehensive comparison of residue-level methylation levels with the regression-based gene-level methylation estimations by ReGear.

MOTIVATION: DNA methylation is a biological process impacting the gene functions without changing the underlying DNA sequence. The DNA methylation machinery usually attaches methyl groups to some specific cytosine residues, which modify the chromatin architectures. Such modifications in the promoter regions will inactivate some tumor-suppressor genes. DNA methylation within the coding region may significantly reduce the transcription elongation efficiency. The gene function may be tuned through some cytosines are methylated. METHODS: This study hypothesizes that the overall methylation level across a gene may have a better association with the sample labels like diseases than the methylations of individual cytosines. The gene methylation level is formulated as a regression model using the methylation levels of all the cytosines within this gene. A comprehensive evaluation of various feature selection algorithms and classification algorithms is carried out between the gene-level and residue-level methylation levels. RESULTS: A comprehensive evaluation was conducted to compare the gene and cytosine methylation levels for their associations with the sample labels and classification performances. The unsupervised clustering was also improved using the gene methylation levels. Some genes demonstrated statistically significant associations with the class label, even when no residue-level methylation features have statistically significant associations with the class label. So in summary, the trained gene methylation levels improved various methylome-based machine learning models. Both methodology development of regression algorithms and experimental validation of the gene-level methylation biomarkers are worth of further investigations in the future studies. The source code, example data files and manual are available at http://www.healthinformaticslab.org/supp/.

Oral cancers preceded by proliferative verrucous leukoplakia exhibit distinctive molecular features.

OBJECTIVE: Proliferative verrucous leukoplakia (PVL) has high rates of malignant transformation into oral squamous cell carcinoma (OSCC), but the clinical and evolutionary pattern of OSCC from PVL (PVL-OSCC) is more favorable than that of OSCC not preceded by PVL (OSCC). Here, we aimed to explore the pathophysiologic differences between PVL-OSCC and OSCC through transcriptomic and DNA methylation analyses. MATERIALS AND METHODS: In this case-control study, oral biopsies from 8 PVL-OSCC and 10 OSCC patients were obtained for global sequencing using RNAseq and a genome-wide DNA methylation analysis via the Infinium EPIC Platform (graphical abstract). RESULTS: One hundred and thirty-three differentially expressed genes (DEGs) were detected, 94 of them upregulated in OSCC. Most of these genes were previously described in cancer and associated with prognosis. The integrative analysis revealed 26 DEGs, corresponding to 37 CpGs, whose promoters were regulated by DNA methylation. Twenty-nine of the CpGs were found as hypermethylated in PVL-OSCC. Only 5 of the genes that were aberrantly methylated and differentially expressed were upregulated in PVL-OSCC patients, whereas 21 were underexpressed. CONCLUSIONS: PVL-OSCC patients presented lower expression of cancer-related genes. Hypermethylation of the promoter region of many genes was also noticed, indicating that DNA methylation could be a regulatory mechanism.

Unravelling Differential DNA Methylation Patterns in Genotype Dependent Manner under Salinity Stress Response in Chickpea.

DNA methylation is one of the epigenetic mechanisms that govern gene regulation in response to abiotic stress in plants. Here, we analyzed the role of epigenetic variations by exploring global DNA methylation and integrating it with differential gene expression in response to salinity stress in tolerant and sensitive chickpea genotypes. Genome-wide DNA methylation profiles showed higher CG methylation in the gene body regions and higher CHH methylation in the TE body regions. The analysis of differentially methylated regions (DMRs) suggested more hyper-methylation in response to stress in the tolerant genotype compared to the sensitive genotype. We observed higher enrichment of CG DMRs in genes and CHH DMRs in transposable elements (TEs). A positive correlation of gene expression with CG gene body methylation was observed. The enrichment analysis of DMR-associated differentially expressed genes revealed they are involved in biological processes, such as lateral root development, transmembrane transporter activity, GTPase activity, and regulation of gene expression. Further, a high correlation of CG methylation with CHG and CHH methylation under salinity stress was revealed, suggesting crosstalk among the methylation contexts. Further, we observed small RNA-mediated CHH hypermethylation in TEs. Overall, the interplay between DNA methylation, small RNAs, and gene expression provides new insights into the regulatory mechanism underlying salinity stress response in chickpeas.

Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms.

Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods' performance and DM signatures' quality in the absence of gold standard data solving a long-existing problem in DM analysis.

Genome-Wide DNA Methylation and Transcriptome Integration Associates DNA Methylation Changes with Bovine Subclinical Mastitis Caused by Staphylococcus chromogenes.

Staphylococcus chromogenes (SC) is a common coagulase-negative staphylococcus described as an emerging mastitis pathogen and commonly found in dairy farms. This study investigated the potential involvement of DNA methylation in subclinical mastitis caused by SC. The whole-genome DNA methylation patterns and transcriptome profiles of milk somatic cells from four cows with naturally occurring SC subclinical mastitis (SCM) and four healthy cows were characterized by next-generation sequencing, bioinformatics, and integration analyses. Comparisons revealed abundant DNA methylation changes related to SCM, including differentially methylated cytosine sites (DMCs, n = 2,163,976), regions (DMRs, n = 58,965), and methylation haplotype blocks (dMHBs, n = 53,098). Integration of methylome and transcriptome data indicated a negative global association between DNA methylation at regulatory regions (promoters, first exons, and first introns) and gene expression. A total of 1486 genes with significant changes in the methylation levels of their regulatory regions and corresponding gene expression showed significant enrichment in biological processes and pathways related to immune functions. Sixteen dMHBs were identified as candidate discriminant signatures, and validation of two signatures in more samples further revealed the association of dMHBs with mammary gland health and production. This study demonstrated abundant DNA methylation changes with possible involvement in regulating host responses and potential as biomarkers for SCM.

Practical application of a Bayesian network approach to poultry epigenetics and stress.

BACKGROUND: Relationships among genetic or epigenetic features can be explored by learning probabilistic networks and unravelling the dependencies among a set of given genetic/epigenetic features. Bayesian networks (BNs) consist of nodes that represent the variables and arcs that represent the probabilistic relationships between the variables. However, practical guidance on how to make choices among the wide array of possibilities in Bayesian network analysis is limited. Our study aimed to apply a BN approach, while clearly laying out our analysis choices as an example for future researchers, in order to provide further insights into the relationships among epigenetic features and a stressful condition in chickens (Gallus gallus). RESULTS: Chickens raised under control conditions (n = 22) and chickens exposed to a social isolation protocol (n = 24) were used to identify differentially methylated regions (DMRs). A total of 60 DMRs were selected by a threshold, after bioinformatic pre-processing and analysis. The treatment was included as a binary variable (control = 0; stress = 1). Thereafter, a BN approach was applied: initially, a pre-filtering test was used for identifying pairs of features that must not be included in the process of learning the structure of the network; then, the average probability values for each arc of being part of the network were calculated; and finally, the arcs that were part of the consensus network were selected. The structure of the BN consisted of 47 out of 61 features (60 DMRs and the stressful condition), displaying 43 functional relationships. The stress condition was connected to two DMRs, one of them playing a role in tight and adhesive intracellular junctions in organs such as ovary, intestine, and brain. CONCLUSIONS: We clearly explain our steps in making each analysis choice, from discrete BN models to final generation of a consensus network from multiple model averaging searches. The epigenetic BN unravelled functional relationships among the DMRs, as well as epigenetic features in close association with the stressful condition the chickens were exposed to. The DMRs interacting with the stress condition could be further explored in future studies as possible biomarkers of stress in poultry species.

Genome-wide differential DNA methylation analysis of MDA-MB-231 breast cancer cells treated with curcumin derivatives, ST08 and ST09.

ST08 and ST09 are potent curcumin derivatives with antiproliferative, apoptotic, and migrastatic properties. Both ST08 and ST09 exhibit in vitro and in vivo anticancer properties. As reported earlier, these derivatives were highly cytotoxic towards MDA-MB-231 triple-negative breast cancer cells with IC50 values in the nanomolar (40-80nM) range.In this study,we performed whole-genome bisulfite sequencing(WGBS) of untreated (control), ST08 and ST09 (treated) triple-negative breast cancer cell line MDA-MB-231 to unravel epigenetic changes induced by the drug. We identified differentially methylated sites (DMSs) enriched in promoter regions across the genome. Analysis of the CpG island promoter methylation identified 12 genes common to both drugs, and 50% of them are known to be methylated in patient samples that were hypomethylated by drugs belonging to the homeobox family transcription factors.Methylation analysis of the gene body revealed 910 and 952 genes to be hypermethylatedin ST08 and ST09 treated MDA-MB-231 cells respectively. Correlation of the gene body hypermethylation with expression revealed CACNAH1 to be upregulated in ST08 treatment and CDH23 upregulation in ST09.Further, integrated analysis of the WGBS with RNA-seq identified uniquely altered pathways - ST08 altered ECM pathway, and ST09 cell cycle, indicating drug-specific signatures.

N6-Methyladenosine dynamic changes and differential methylation in wheat grain development.

MAIN CONCLUSION: More methylation changes occur in late interval than in early interval of wheat seed development with protein and the starch synthesis-related pathway enriched in the later stages. Wheat seed development is a critical process to determining wheat yield and quality, which is controlled by genetics, epigenetics and environments. The N6-methyladenosine (m6A) modification is a reversible and dynamic process and plays regulatory role in plant development and stress responses. To better understand the role of m6A in wheat grain development, we characterized the m6A modification at 10 day post-anthesis (DPA), 20 DPA and 30 DPA in wheat grain development. m6A-seq identified 30,615, 30,326, 27,676 high confidence m6A peaks from the 10DPA, 20DPA, and 30DPA, respectively, and enriched at 3'UTR. There were 29,964, 29,542 and 26,834 unique peaks identified in AN0942_10d, AN0942_20d and AN0942_30d. One hundred and forty-two genes were methylated by m6A throughout seed development, 940 genes methylated in early grain development (AN0942_20d vs AN0942_10d), 1542 genes in late grain development (AN0942_30d vs AN0942_20d), and 1190 genes between early and late development stage (AN0942_30d vs AN0942_10d). KEGG enrichment analysis found that protein-related pathways and the starch synthesis-related pathway were significantly enriched in the later stages of seed development. Our results provide novel knowledge on m6A dynamic changes and its roles in wheat grain development.

Multi-omics analysis of tumor angiogenesis characteristics and potential epigenetic regulation mechanisms in renal clear cell carcinoma.

BACKGROUND: Tumor angiogenesis, an essential process for cancer proliferation and metastasis, has a critical role in prognostic of kidney renal clear cell carcinoma (KIRC), as well as a target in guiding treatment with antiangiogenic agents. However, tumor angiogenesis subtypes and potential epigenetic regulation mechanisms in KIRC patient remains poorly characterized. System evaluation of angiogenesis subtypes in KIRC patient might help to reveal the mechanisms of KIRC and develop more target treatments for patients. METHOD: Ten independent tumor angiogenesis signatures were obtained from molecular signatures database (MSigDB) and gene set variation analysis was performed to calculate the angiogenesis score in silico using the Cancer Genome Atlas (TCGA) KIRC dataset. Tumor angiogenesis subtypes in 539 TCGA-KIRC patients were identified using consensus clustering analysis. The potential regulation mechanisms was studied using gene mutation, copy number variation, and differential methylation analysis (DMA). The master transcription factors (MTF) that cause the difference in tumor angiogenesis signals were completed by transcription factor enrichment analysis. RESULTS: The angiogenesis score of a prognosis related angiogenesis signature including 189 genes was significantly correlated with immune score, stroma score, hypoxia score, and vascular endothelial growth factor (VEGF) signal score in 539 TCGA KIRC patients. MMRN2, CLEC14A, ACVRL1, EFNB2, and TEK in candidate gene set showed highest correlation coefficient with angiogenesis score in TCGA-KIRC patients. In addition, all of them were associated with overall survival in both TCGA-KIRC and E-MTAB-1980 KIRC data. Clustering analysis based on 183 genes in angiogenesis signature identified two prognosis related angiogenesis subtypes in TCGA KIRC patients. Two clusters also showed different angiogenesis score, immune score, stroma score, hypoxia score, VEGF signal score, and microenvironment score. DMA identified 59,654 differential methylation sites between two clusters and part of these sites were correlated with tumor angiogenesis genes including CDH13, COL4A3, and RHOB. In addition, RFX2, SOX13, and THRA were identified as top three MTF in regulating angiogenesis signature in KIRC patients. CONCLUSION: Our study indicate that evaluation the angiogenesis subtypes of KIRC based on angiogenesis signature with 183 genes and potential epigenetic mechanisms may help to develop more target treatments for KIRC patients. Video Abstract.

Predicting amplification of MYCN using CpG methylation biomarkers in neuroblastoma.

Background: Neuroblastoma is the most common extracranial solid tumor in childhood. Amplification of MYCN in neuroblastoma is a predictor of poor prognosis. Materials and methods: DNA methylation data from the TARGET data matrix were stratified into MYCN amplified and non-amplified groups. Differential methylation analysis, clustering, recursive feature elimination (RFE), machine learning (ML), Cox regression analysis and Kaplan-Meier estimates were performed. Results and Conclusion: 663 CpGs were differentially methylated between the two groups. A total of 25 CpGs were selected by RFE for clustering and ML, and a 100% clustering accuracy was obtained. ML validation on three external datasets produced high accuracy scores of 100%, 97% and 93%. Eight survival-associated CpGs were also identified. Therapeutic interventions may need to be targeted to patient subgroups.

Lay abstract Neuroblastoma is the most common extracranial solid tumor in childhood. Elevated levels of the MYCN protein in neuroblastoma is a predictor of poor prognosis. It is the most relevant prognostic factor in neuroblastoma and predicting MYCN gene amplification (which leads to increased gene expression and more protein) from epigenetic data rather than genetic testing might be useful in the oncology clinic. This study was designed to identify a DNA methylation (epigenetic) signature that can be used to diagnose MYCN amplification without actually testing for the gene. The authors also aimed to correlate this DNA methylation signature with patient survival and poorer prognosis. Based on statistical and computational methods applied to DNA methylation data for neuroblastoma, signatures that are predictive of MYCN amplification and poor prognosis were found, which clinicians can use for early patient diagnosis and selection of the best therapies for patients at high risk.

MADA: a web service for analysing DNA methylation array data.

BACKGROUND: DNA methylation in the human genome is acknowledged to be widely associated with biological processes and complex diseases. The Illumina Infinium methylation arrays have been approved as one of the most efficient and universal technologies to investigate the whole genome changes of methylation patterns. As methylation arrays may still be the dominant method for detecting methylation in the anticipated future, it is crucial to develop a reliable workflow to analysis methylation array data. RESULTS: In this study, we develop a web service MADA for the whole process of methylation arrays data analysis, which includes the steps of a comprehensive differential methylation analysis pipeline: pre-processing (data loading, quality control, data filtering, and normalization), batch effect correction, differential methylation analysis, and downstream analysis. In addition, we provide the visualization of pre-processing, differentially methylated probes or regions, gene ontology, pathway and cluster analysis results. Moreover, a customization function for users to define their own workflow is also provided in MADA. CONCLUSIONS: With the analysis of two case studies, we have shown that MADA can complete the whole procedure of methylation array data analysis. MADA provides a graphical user interface and enables users with no computational skills and limited bioinformatics background to carry on complicated methylation array data analysis. The web server is available at: http://120.24.94.89:8080/MADA.

PretiMeth: precise prediction models for DNA methylation based on single methylation mark.

BACKGROUND: The computational prediction of methylation levels at single CpG resolution is promising to explore the methylation levels of CpGs uncovered by existing array techniques, especially for the 450 K beadchip array data with huge reserves. General prediction models concentrate on improving the overall prediction accuracy for the bulk of CpG loci while neglecting whether each locus is precisely predicted. This leads to the limited application of the prediction results, especially when performing downstream analysis with high precision requirements. RESULTS: Here we reported PretiMeth, a method for constructing precise prediction models for each single CpG locus. PretiMeth used a logistic regression algorithm to build a prediction model for each interested locus. Only one DNA methylation feature that shared the most similar methylation pattern with the CpG locus to be predicted was applied in the model. We found that PretiMeth outperformed other algorithms in the prediction accuracy, and kept robust across platforms and cell types. Furthermore, PretiMeth was applied to The Cancer Genome Atlas data (TCGA), the intensive analysis based on precise prediction results showed that several CpG loci and genes (differentially methylated between the tumor and normal samples) were worthy for further biological validation. CONCLUSION: The precise prediction of single CpG locus is important for both methylation array data expansion and downstream analysis of prediction results. PretiMeth achieved precise modeling for each CpG locus by using only one significant feature, which also suggested that our precise prediction models could be probably used for reference in the probe set design when the DNA methylation beadchip update. PretiMeth is provided as an open source tool via https://github.com/JxTang-bioinformatics/PretiMeth.

Detection and accurate false discovery rate control of differentially methylated regions from whole genome bisulfite sequencing.

With recent advances in sequencing technology, it is now feasible to measure DNA methylation at tens of millions of sites across the entire genome. In most applications, biologists are interested in detecting differentially methylated regions, composed of multiple sites with differing methylation levels among populations. However, current computational approaches for detecting such regions do not provide accurate statistical inference. A major challenge in reporting uncertainty is that a genome-wide scan is involved in detecting these regions, which needs to be accounted for. A further challenge is that sample sizes are limited due to the costs associated with the technology. We have developed a new approach that overcomes these challenges and assesses uncertainty for differentially methylated regions in a rigorous manner. Region-level statistics are obtained by fitting a generalized least squares regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. We develop an inferential approach, based on a pooled null distribution, that can be implemented even when as few as two samples per population are available. Here, we demonstrate the advantages of our method using both experimental data and Monte Carlo simulation. We find that the new method improves the specificity and sensitivity of lists of regions and accurately controls the false discovery rate.

EBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples.

DNA methylation and gene expression are interdependent and both implicated in cancer development and progression, with many individual biomarkers discovered. A joint analysis of the two data types can potentially lead to biological insights that are not discoverable with separate analyses. To optimally leverage the joint data for identifying perturbed genes and classifying clinical cancer samples, it is important to accurately model the interactions between the two data types. Here, we present EBADIMEX for jointly identifying differential expression and methylation and classifying samples. The moderated t-test widely used with empirical Bayes priors in current differential expression methods is generalised to a multivariate setting by developing: (1) a moderated Welch t-test for equality of means with unequal variances; (2) a moderated F-test for equality of variances; and (3) a multivariate test for equality of means with equal variances. This leads to parametric models with prior distributions for the parameters, which allow fast evaluation and robust analysis of small data sets. EBADIMEX is demonstrated on simulated data as well as a large breast cancer (BRCA) cohort from TCGA. We show that the use of empirical Bayes priors and moderated tests works particularly well on small data sets.

Transgenerational epigenetic and transcriptomic effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin exposure in rat.

In rats, direct exposure to TCDD causes myriad toxicities. Exposed rats experience hepatotoxicity, wasting syndrome and immune suppression, amongst others. "Inherited exposure", as occurs in the F3 generation of directly exposed F0 animals, has also been shown to cause toxicity: both male and female F3 rats demonstrate an increased incidence of adult onset disease, females also display reproductive abnormalities and increased incidence of ovarian diseases while males show increased incidence of kidney disease and an altered sperm epigenome. Here, we explore the hepatic transcriptomic profile of male and female F3 Sprague-Dawley rats bred through the paternal germ line from F0 dams exposed to a single dose of TCDD (0, 30, 100, 300 or 1000 ng/kg body weight) by oral gavage. We hypothesize that RNA transcripts with altered abundance in livers of unexposed F3 progeny of treated F0 Sprague-Dawley rats may result from epigenetic modifications to the genome. We further survey patterns of differential methylation within male F3 rat testis. Female F3 rats demonstrated more TCDD-mediated hepatic transcriptomic changes than males, with differences primarily in the lowest dose group. In testis from male F3 rats, multiple olfactory receptors displayed patterns of differential methylation. Hypermethylation of Egfr and Mc5r among testes from TCDD lineage rats was observed, but without corresponding changes in hepatic mRNA abundance. Further studies examining these differences in other tissue types are warranted.

PEIS: a novel approach of tumor purity estimation by identifying information sites through integrating signal based on DNA methylation data.

BACKGROUND: Tumor purity plays an important role in understanding the pathogenic mechanism of tumors. The purity of tumor samples is highly sensitive to tumor heterogeneity. Due to Intratumoral heterogeneity of genetic and epigenetic data, it is suitable to study the purity of tumors. Among them, there are many purity estimation methods based on copy number variation, gene expression and other data, while few use DNA methylation data and often based on selected information sites. Consequently, how to choose methylation sites as information sites has an important influence on the purity estimation results. At present, the selection of information sites was often based on the differentially methylated sites that only consider the mean signal, without considering other possible signals and the strong correlation among adjacent sites. RESULTS: Considering integrating multi-signals and strong correlation among adjacent sites, we propose an approach, PEIS, to estimate the purity of tumor samples by selecting informative differential methylation sites. Application to 12 publicly available tumor datasets, it is shown that PEIS provides accurate results in the estimation of tumor purity which has a high consistency with other existing methods. Also, through comparing the results of different information sites selection methods in the evaluation of tumor purity, it shows the PEIS is superior to other methods. CONCLUSIONS: A new method to estimate the purity of tumor samples is proposed. This approach integrates multi-signals of the CpG sites and the correlation between the sites. Experimental analysis shows that this method is in good agreement with other existing methods for estimating tumor purity.